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egs/rm/s5/local/online/run_nnet2_multisplice_disc.sh
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#!/bin/bash
# This is to be run after run_nnet2_multisplice.sh.
# It demonstrates discriminative training for the online-nnet2 models
. ./cmd.sh
stage=1
train_stage=-10
use_gpu=true
srcdir=exp/nnet2_online/nnet_ms_a_online
criterion=smbr
learning_rate=0.0016
drop_frames=false # only relevant for MMI
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if [ ! -f $srcdir/final.mdl ]; then
echo "$0: expected $srcdir/final.mdl to exist; first run run_nnet2_multisplice.sh."
exit 1;
fi
if $use_gpu; then
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed. Otherwise, call this script with --use-gpu false
EOF
fi
parallel_opts="--gpu 1"
num_threads=1
else
# Use 4 nnet jobs just like run_4d_gpu.sh so the results should be
# almost the same, but this may be a little bit slow.
num_threads=16
parallel_opts="--num-threads $num_threads"
fi
if [ $stage -le 1 ]; then
# the conf/decode.config gives it higher than normal beam/lattice-beam of (20,10), since
# otherwise on RM we'd get very thin lattices.
nj=30
num_threads_denlats=6
steps/online/nnet2/make_denlats.sh --cmd "$decode_cmd --mem 1G --num-threads $num_threads_denlats" \
--nj $nj --sub-split 40 --num-threads "$num_threads_denlats" --config conf/decode.config \
data/train data/lang $srcdir ${srcdir}_denlats || exit 1;
fi
if [ $stage -le 2 ]; then
# hardcode no-GPU for alignment, although you could use GPU [you wouldn't
# get excellent GPU utilization though.]
nj=100
use_gpu=no
gpu_opts=
steps/online/nnet2/align.sh --cmd "$decode_cmd $gpu_opts" --use-gpu "$use_gpu" \
--nj $nj data/train data/lang $srcdir ${srcdir}_ali || exit 1;
fi
if [ $stage -le 3 ]; then
# I tested the following with --max-temp-archives 3
# to test other branches of the code.
# the --max-jobs-run 5 limits the I/O.
steps/online/nnet2/get_egs_discriminative2.sh \
--cmd "$decode_cmd --max-jobs-run 5" \
--criterion $criterion --drop-frames $drop_frames \
data/train data/lang ${srcdir}{_ali,_denlats,,_degs} || exit 1;
fi
if [ $stage -le 4 ]; then
steps/nnet2/train_discriminative2.sh --cmd "$decode_cmd $parallel_opts" \
--learning-rate $learning_rate \
--criterion $criterion --drop-frames $drop_frames \
--num-epochs 6 \
--num-jobs-nnet 2 --num-threads $num_threads \
${srcdir}_degs ${srcdir}_${criterion}_${learning_rate} || exit 1;
fi
if [ $stage -le 5 ]; then
ln -sf $(utils/make_absolute.sh $srcdir/conf) ${srcdir}_${criterion}_${learning_rate}/conf # so it acts like an online-decoding directory
for epoch in 0 1 2 3 4 5 6; do
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
--iter epoch$epoch exp/tri3b/graph data/test ${srcdir}_${criterion}_${learning_rate}/decode_epoch$epoch &
steps/online/nnet2/decode.sh --config conf/decode.config --cmd "$decode_cmd" --nj 20 \
--iter epoch$epoch exp/tri3b/graph_ug data/test ${srcdir}_${criterion}_${learning_rate}/decode_ug_epoch$epoch &
done
wait
for dir in ${srcdir}_${criterion}_${learning_rate}/decode*; do grep WER $dir/wer_* | utils/best_wer.sh; done
fi
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